OpenRaiser/Intern-Atlas huggingface dataset:
PVU: Design and Implementation of a Posit Vector Arithmetic Unit (PVU) for Enhanced Floating-Point Computing in Edge and AI Applications
Venue: arXiv cs.DC 2025
Year: 2025

With the rapid development of edge computing, artificial intelligence and other fields, the accuracy and efficiency of floating-point computing have become increasingly crucial. However, the traditional IEEE 754 floating-point system faces bottlenecks in energy consumption and computing accuracy, which have become major constraints. To address this issue, the Posit digital system characterized by adaptive accuracy, broader dynamic range and low hardware consumption has been put forward. Despite its widespread adoption, the existing research mainly concentrates on scalar computation, which is insufficient to meet the requirements of large-scale parallel data processing. This paper proposes, for the first time, a Posit Vector Arithmetic Unit (PVU) designed using the Chisel language. It supports vector operations such as addition, subtraction, multiplication, division, and dot product, thereby overcoming the limitations of traditional scalar designs and integrating the RISC-V instruction extension. The contributions of this paper include the efficient implementation of the vector arithmetic unit, the parametric and modular hardware design as well as the verification of the practical application of the positive digital system. This paper extracts the quantized data of the first convolutional layer for verification. Experiments indicate that the accuracy rate of the division operation is 95.84\%, and the accuracy rate of the remaining operations is 100\%. Moreover, the PVU is implemented with only 65,407 LUTs. Therefore, PVU has great potential as a new-generation floating-point computing platform in various fields.
WeightedKV: Attention Scores Weighted Key-Value Cache Merging for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) use key-value (KV) cache to reduce redundant computation in autoregressive generation. However, the KV cache size increases linearly during generation, leading to excessive memory usage, especially for long texts. Most KV cache compression methods evict the unimportant KV pairs to maintain a fixed cache size, which leads to the permanent loss of tokens during generation. However, singular value decomposition shows that \textit{values} do not exhibit a strong low-rank property as \textit{keys} do, suggesting that information is distributed more evenly across \textit{values}, in contrast to its more redundant distribution within \textit{keys}. Therefore, methods that evict both \textit{keys} and \textit{values} risk losing crucial information and compromise context integrity, ultimately degrading the output quality. To address this problem, we propose WeightedKV, a novel, training-free approach that discards the \textit{keys} of less important tokens, while merging their \textit{values} into neighboring tokens via a convex combination weighted by their average attention scores. In this way, the retained \textit{keys} serve as anchors that guide the generation process, while the merged \textit{values} provide a rich contextual backdrop. We assess our method on four widely used language modeling datasets, demonstrating superior performance compared to all baseline methods, particularly with a lower budget ratio.
Performance Optimization of 3D Stencil Computation on ARM Scalable Vector Extension
Venue: arXiv cs.PF 2025
Year: 2025

Stencil computation is essential in high-performance computing, especially for large-scale tasks like liquid simulation and weather forecasting. Optimizing its performance can reduce both energy consumption and computation time, which is critical in disaster prediction. This paper explores optimization techniques for 7-point 3D stencil computation on ARM's Scalable Vector Extension (SVE), using the Roofline model and tools like Gem5 and cacti. We evaluate software optimizations such as vectorization and tiling, as well as hardware adjustments in ARM SVE vector lengths and cache configurations. The study also examines performance, power consumption, and chip area trade-offs to identify optimal configurations for ARM-based systems.
Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning
Venue: arXiv cs.LG 2025
Year: 2025

Human activity recognition (HAR) is a research field that employs Machine Learning (ML) techniques to identify user activities. Recent studies have prioritized the development of HAR solutions directly executed on wearable devices, enabling the on-device activity recognition. This approach is supported by the Tiny Machine Learning (TinyML) paradigm, which integrates ML within embedded devices with limited resources. However, existing approaches in the field lack in the capability for on-device learning of new HAR tasks, particularly when supervised data are scarce. To address this limitation, our paper introduces Dendron, a novel TinyML methodology designed to facilitate the on-device learning of new tasks for HAR, even in conditions of limited supervised data. Experimental results on two public-available datasets and an off-the-shelf device (STM32-NUCLEO-F401RE) show the effectiveness and efficiency of the proposed solution.
Streaming Piano Transcription Based on Consistent Onset and Offset Decoding with Sustain Pedal Detection
Venue: arXiv cs.SD 2025
Year: 2025

This paper describes a streaming audio-to-MIDI piano transcription approach that aims to sequentially translate a music signal into a sequence of note onset and offset events. The sequence-to-sequence nature of this task may call for the computationally-intensive transformer model for better performance, which has recently been used for offline transcription benchmarks and could be extended for streaming transcription with causal attention mechanisms. We assume that the performance limitation of this naive approach lies in the decoder. Although time-frequency features useful for onset detection are considerably different from those for offset detection, the single decoder is trained to output a mixed sequence of onset and offset events without guarantee of the correspondence between the onset and offset events of the same note. To overcome this limitation, we propose a streaming encoder-decoder model that uses a convolutional encoder aggregating local acoustic features, followed by an autoregressive Transformer decoder detecting a variable number of onset events and another decoder detecting the offset events for the active pitches with validation of the sustain pedal at each time frame. Experiments using the MAESTRO dataset showed that the proposed streaming method performed comparably with or even better than the state-of-the-art offline methods while significantly reducing the computational cost.
Faster ED-String Matching with $k$ Mismatches
Venue: arXiv cs.DS 2025
Year: 2025

We revisit the complexity of approximate pattern matching in an elastic-degenerate string. Such a string is a sequence of $n$ finite sets of strings of total length $N$, and compactly describes a collection of strings obtained by first choosing exactly one string in every set, and then concatenating them together. This is motivated by the need of storing a collection of highly similar DNA sequences. The basic algorithmic question on elastic-degenerate strings is pattern matching: given such an elastic-degenerate string and a standard pattern of length $m$, check if the pattern occurs in one of the strings in the described collection. Bernardini et al.~[SICOMP 2022] showed how to leverage fast matrix multiplication to obtain an $\tilde{\mathcal{O}}(nm^{ω-1})+\mathcal{O}(N)$-time complexity for this problem, where $w$ is the matrix multiplication exponent. However, the best result so far for finding occurrences with $k$ mismatches, where $k$ is a constant, is the $\tilde{\mathcal{O}}(nm^{2}+N)$-time algorithm of Pissis et al.~[CPM 2025]. This brings the question whether increasing the dependency on $m$ from $m^{ω-1}$ to quadratic is necessary when moving from $k=0$ to larger (but still constant) $k$. We design an $\tilde{\mathcal{O}}(nm^{1.5}+N)$-time algorithm for pattern matching with $k$ mismatches in an elastic-degenerate string, for any constant $k$. To obtain this time bound, we leverage the structural characterization of occurrences with $k$ mismatches of Charalampopoulos et al.~[FOCS 2020] together with fast Fourier transform. We need to work with multiple patterns at the same time, instead of a single pattern, which requires refining the original characterization. This might be of independent interest.
Jailbreaking Generative AI: Empowering Novices to Conduct Phishing Attacks
Venue: arXiv cs.CR 2025
Year: 2025

The rapid advancements in generative AI models, such as ChatGPT, have introduced both significant benefits and new risks within the cybersecurity landscape. This paper investigates the potential misuse of the latest AI model, ChatGPT-4o Mini, in facilitating social engineering attacks, with a particular focus on phishing, one of the most pressing cybersecurity threats today. While existing literature primarily addresses the technical aspects, such as jailbreaking techniques, none have fully explored the free and straightforward execution of a comprehensive phishing campaign by novice users using ChatGPT-4o Mini. In this study, we examine the vulnerabilities of AI-driven chatbot services in 2025, specifically how methods like jailbreaking and reverse psychology can bypass ethical safeguards, allowing ChatGPT to generate phishing content, suggest hacking tools, and assist in carrying out phishing attacks. Our findings underscore the alarming ease with which even inexperienced users can execute sophisticated phishing campaigns, emphasizing the urgent need for stronger cybersecurity measures and heightened user awareness in the age of AI.
Building Interval Type-2 Fuzzy Membership Function: A Deck of Cards based Co-constructive Approach
Venue: arXiv cs.AI 2025
Year: 2025

Since its inception, Fuzzy Set has been widely used to handle uncertainty and imprecision in decision-making. However, conventional fuzzy sets, often referred to as type-1 fuzzy sets (T1FSs) have limitations in capturing higher levels of uncertainty, particularly when decision-makers (DMs) express hesitation or ambiguity in membership degree. To address this, Interval Type-2 Fuzzy Sets (IT2FSs) have been introduced by incorporating uncertainty in membership degree allocation, which enhanced flexibility in modelling subjective judgments. Despite their advantages, existing IT2FS construction methods often lack active involvement from DMs and that limits the interpretability and effectiveness of decision models. This study proposes a socio-technical co-constructive approach for developing IT2FS models of linguistic terms by facilitating the active involvement of DMs in preference elicitation and its application in multicriteria decision-making (MCDM) problems. Our methodology is structured in two phases. The first phase involves an interactive process between the DM and the decision analyst, in which a modified version of Deck-of-Cards (DoC) method is proposed to construct T1FS membership functions on a ratio scale. We then extend this method to incorporate ambiguity in subjective judgment and that resulted in an IT2FS model that better captures uncertainty in DM's linguistic assessments. The second phase formalizes the constructed IT2FS model for application in MCDM by defining an appropriate mathematical representation of such information, aggregation rules, and an admissible ordering principle. The proposed framework enhances the reliability and effectiveness of fuzzy decision-making not only by accurately representing DM's personalized semantics of linguistic information.
Improving the Efficiency of VVC using Partitioning of Reference Frames
Venue: arXiv cs.MM 2025
Year: 2025

In response to the growing demand for high-quality videos, Versatile Video Coding (VVC) was released in 2020, building on the hybrid coding architecture of its predecessor, HEVC, achieving about 50% bitrate reduction for the same visual quality. It introduces more flexible block partitioning, enhancing compression efficiency at the cost of increased encoding complexity. To make efficient use of VVC in practical applications, optimization is essential. VVenC, an optimized open-source VVC encoder, introduces multiple presets to address the trade-off between compression efficiency and encoder complexity. Although an optimized set of encoding tools has been selected for each preset, the rate-distortion (RD) search space in the encoder presets still poses a challenge for efficient encoder implementations. In this paper, we propose Early Termination using Reference Frames (ETRF), which improves the trade-off between encoding efficiency and time complexity and positions itself as a new preset between medium and fast presets. The CTU partitioning map of the reference frames in lower temporal layers is employed to accelerate the encoding of frames in higher temporal layers. The results show a reduction in the encoding time of around 21% compared to the medium preset. Specifically, for videos with high spatial and temporal complexities, which typically require longer encoding times, the proposed method achieves a better trade-off between bitrate savings and encoding time compared to the fast preset.
Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions
Venue: arXiv cs.SI 2025
Year: 2025

Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on users' posts on Reddit. To classify the disorders, we use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data. We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy. Our hybrid approach combines the generalization capabilities of pre-trained models with the domain-specific insights captured by LLMs, providing an improved understanding of mental health discourse. Our findings highlight the strengths and limitations of each model, offering valuable insights into their practical applicability. This research potentially facilitates early detection and personalized care to aid practitioners and aims to facilitate timely interventions and improve overall well-being, thereby contributing to the broader field of mental health surveillance and digital health analytics.
Proper decision trees: An axiomatic framework for solving optimal decision tree problems with arbitrary splitting rules
Venue: arXiv cs.LG 2025
Year: 2025

We present an axiomatic framework for analyzing the algorithmic properties of decision trees. This framework supports the classification of decision tree problems through structural and ancestral constraints within a rigorous mathematical foundation. The central focus of this paper is a special class of decision tree problems-which we term proper decision trees-due to their versatility and effectiveness. In terms of versatility, this class subsumes several well-known data structures, including binary space partitioning trees, K-D trees, and machine learning decision tree models. Regarding effectiveness, we prove that only proper decision trees can be uniquely characterized as K-permutations, whereas typical non-proper decision trees correspond to binary-labeled decision trees with substantially greater complexity. Using this formal characterization, we develop a generic algorithmic approach for solving optimal decision tree problems over arbitrary splitting rules and objective functions for proper decision trees. We constructively derive a generic dynamic programming recursion for solving these problems exactly. However, we show that memoization is generally impractical in terms of space complexity, as both datasets and subtrees must be stored. This result contradicts claims in the literature that suggest a trade-off between memoizing datasets and subtrees. Our framework further accommodates constraints such as tree depth and leaf size, and can be accelerated using techniques such as thinning. Finally, we extend our analysis to several non-proper decision trees, including the commonly studied decision tree over binary feature data, the binary search tree, and the tree structure arising in the matrix chain multiplication problem. We demonstrate how these problems can be solved by appropriately modifying or discarding certain axioms.
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Continuous control of non-stationary environments is a major challenge for deep reinforcement learning algorithms. The time-dependency of the state transition dynamics aggravates the notorious stability problems of model-free deep actor-critic architectures. We posit that two properties will play a key role in overcoming non-stationarity in transition dynamics: (i)~preserving the plasticity of the critic network and (ii) directed exploration for rapid adaptation to changing dynamics. We show that performing on-policy reinforcement learning with an evidential critic provides both. The evidential design ensures a fast and accurate approximation of the uncertainty around the state value, which maintains the plasticity of the critic network by detecting the distributional shifts caused by changes in dynamics. The probabilistic critic also makes the actor training objective a random variable, enabling the use of directed exploration approaches as a by-product. We name the resulting algorithm \emph{Evidential Proximal Policy Optimization (EPPO)} due to the integral role of evidential uncertainty quantification in both policy evaluation and policy improvement stages. Through experiments on non-stationary continuous control tasks, where the environment dynamics change at regular intervals, we demonstrate that our algorithm outperforms state-of-the-art on-policy reinforcement learning variants in both task-specific and overall return.
SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity
Venue: arXiv cs.CL 2025
Year: 2025

Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. Further, uniform sampling within domains ignores fine-grained sample-specific features, potentially leading to suboptimal data distribution. To address these shortcomings, we propose a novel sample-wise data mixture approach based on a bottom-up paradigm. This method performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample, thereby dynamically determining the optimal domain distribution. Comprehensive experiments across multiple downstream tasks and perplexity assessments demonstrate that SampleMix surpasses existing domain-based methods. Meanwhile, SampleMix requires 1.4x to 2.1x training steps to achieves the baselines' performance, highlighting the substantial potential of SampleMix to optimize pre-training data.
R2VF: A Two-Step Regularization Algorithm to Cluster Categories in GLMs
Venue: arXiv cs.LG 2025
Year: 2025

Over recent decades, extensive research has aimed to overcome the restrictive underlying assumptions required for a Generalized Linear Model to generate accurate and meaningful predictions. These efforts include regularizing coefficients, selecting features, and clustering ordinal categories, among other approaches. Despite these advances, efficiently clustering nominal categories in GLMs without incurring high computational costs remains a challenge. This paper introduces Ranking to Variable Fusion (R2VF), a two-step method designed to efficiently fuse nominal and ordinal categories in GLMs. By first transforming nominal features into an ordinal framework via regularized regression and then applying variable fusion, R2VF strikes a balance between model complexity and interpretability. We demonstrate the effectiveness of R2VF through comparisons with other methods, highlighting its performance in addressing overfitting and identifying an appropriate set of covariates.
Unit Edge-Length Rectilinear Drawings with Crossings and Rectangular Faces
Venue: arXiv cs.CG 2025
Year: 2025

Unit edge-length drawings, rectilinear drawings (where each edge is either a horizontal or a vertical segment), and rectangular face drawings are among the most studied subjects in Graph Drawing. However, most of the literature on these topics refers to planar graphs and planar drawings. In this paper we study drawings with all the above nice properties but that can have edge crossings; we call them Unit Edge length Rectilinear drawings with Rectangular Faces (UER-RF drawings). We consider crossings as dummy vertices and apply the unit edge-length convention to the edge segments connecting any two (real or dummy) vertices. Note that UER-RF drawings are grid drawings (vertices are placed at distinct integer coordinates), which is another classical requirement of graph visualizations. We present several efficient and easily implementable algorithms for recognizing graphs that admit UER-RF drawings and for constructing such drawings if they exist. We consider restrictions on the degree of the vertices or on the size of the faces. For each type of restriction, we consider both the general unconstrained setting and a setting in which either the external boundary of the drawing is fixed or the rotation system of the graph is fixed as part of the input.
MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network
Venue: arXiv cs.LG 2025
Year: 2025

Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). MoCFL enhances feature aggregation by introducing an affinity matrix that quantifies the similarity between local feature extractors from different clients, addressing dynamic data distribution changes caused by frequent client churn and topology changes. Additionally, MoCFL integrates historical and current feature information when training the global classifier, effectively mitigating the catastrophic forgetting problem frequently encountered in mobile scenarios. This synergistic combination ensures that MoCFL maintains high performance and stability in dynamically changing mobile environments. Experimental results on the UNSW-NB15 dataset show that MoCFL excels in dynamic environments, demonstrating superior robustness and accuracy while maintaining reasonable training costs.